Operational analytics in managed networks

ABSTRACT

A computing system and method for remote monitoring and forecasting of performance of a managed network is disclosed. The computing system may be disposed within a remote network management platform and be configured for monitoring respective performance of each of a plurality of network entities of the managed network. For each network entity, an alert may be issued in response to determining that the monitored respective performance is below a respective threshold performance level. Based on analysis of a group of alerts, a likelihood may be determined that a different alert will be issued for the monitored performance of a particular network entity of the plurality for which no respective alert has yet been issued. In response to the likelihood exceeding a threshold, an alert prediction for the performance of the particular network entity may be issued together with a score corresponding to the likelihood.

BACKGROUND

As an enterprise employs more and remotely managed networks that includemore and more devices and services, it becomes difficult to monitorperformance in a manner that can keep pace with the speed at which andways with which anomalies can escalate into problems. The enterprisemight have tools with which to discover events after they happen. Thesetools, however, are insufficient for rapidly assessing how observedperformance in one device or service may later impact performance ofanother device or service. As a consequence, a network manager of anenterprise network is often playing catch-up when faced with performanceissues.

SUMMARY

It is now common for enterprise networks to include tens of thousands ofdevices across dozens of networks, supporting thousands of users.Enterprise networks may be deployed as remotely managed networks, inwhich many aspects of the actual underlying network architecture, aswell as network operations, are managed offsite by a third party. One ofthe operational missions of remote network management is monitoring andmanaging performance of the devices and services deployed in thenetwork. Advantageously, techniques and systems described here enablenetwork performance management to look ahead, rather than just react.

In a managed network, operational performance of servers (and othercomputing devices) and services may be monitored by a management server.The managed network may include various servers, client devices, andother computing devices, as well as switches, routers, gateways, and thelike, all interconnected in one or more enterprise networks, possiblyincluding multiple subnets. Subnets may be deployed across multiplephysical sites, which may be interconnected by a public internet orother backbone for example. The management server may be locatedremotely from the one or more sites making up a managed network, and mayitself be implemented in a distributed fashion.

The services, computing devices, servers, and network infrastructuredevices (switches, routers, gateways, etc.) may all be manageable.Generically referred to herein as “manageable network entities” (or just“network entities”) each may include configurable hardware and/orsoftware, as well as functionality to report operational and/orperformance status and to receive and execute management-relatedcommands, for example.

Various services running in the managed network may be distributedacross multiple servers and/or other computing platforms or devices. Assuch, performance of these services may have dependencies on networkinterconnections and devices, such as routers and switches, whichprovide the interconnections. Performance may also depend on demand andprocessing load, among other factors. Functional dependencies betweendifferent services may also exist, further adding to service performancedependencies on network performance and network interconnections.

The complexity of interconnections and interdependencies presentschallenges for managing network operational performance in a way thatcan not only monitor real-time performance metrics, but also provideinformation that can be predictive of future, possibly imminent, issuesand/or problems. Advantageously, this challenge can be met byconstructing one or more sets of performance analytics of bothindividual and groups of manageable network entities that are tracked ormonitored by way of status alerts, which in turn are jointly analyzed todetermine specific performance interdependencies between the servicesand servers of any given group. Analytical associations between servicesand servers can be used to derive conditional probabilities relatingobserved alerts to predicted alerts. Applying joint analysis tohistorical alerts may thus be used to provide predictions of futureperformance of various services and/or servers of a given group based onactual, real-time performance of other services and/or servers of thegiven group. In this way, operational performance of services andservers of a managed network may be forecast, enabling prediction ofpotential problems before they arise. Preventive and/or preemptiveactions may be taken to avoid such potential problems.

Accordingly, a first example embodiment may involve a computing systemdisposed within a remote network management platform and configured tosupport a managed network, the computing system comprising: one or moreprocessors; memory; and program instructions, stored in the memory, thatupon execution by the one or more processors cause the computing systemto perform operations including: monitoring respective performance ofeach network entity of a plurality of network entities of the managednetwork, each network entity being at least one of a service of themanaged network or a computing device of the managed network, whereineach service of the managed network executes on at least one computingdevice of the managed network; for each of the plurality of networkentities, issuing an alert in response to determining that the monitoredrespective performance is below a respective threshold performancelevel; based on analysis of a first group of one or more issued alerts,determining a statistical likelihood that a different alert will beissued for the monitored respective performance of a particular networkentity of the plurality for which no respective alert has yet beenissued; and issuing a score notification for the different alert inresponse to the determined statistical likelihood exceeding a scorethreshold, wherein the score notification includes the determinedstatistical likelihood and an identity of the particular network entity.

A second example embodiment may involve, a computer-implemented methodcarried out by a computing system disposed within a remote networkmanagement platform and configured to support a managed network, themethod comprising: monitoring respective performance of each networkentity of a plurality of network entities of the managed network, eachnetwork entity being at least one of a service of the managed network ora computing device of the managed network, wherein each service of themanaged network executes on at least one computing device of the managednetwork; for each of the plurality of network entities, issuing an alertin response to determining that the monitored respective performance isbelow a respective threshold performance level; based on analysis of afirst group of one or more issued alerts, determining a statisticallikelihood that a different alert will be issued for the monitoredrespective performance of a particular network entity of the pluralityfor which no respective alert has yet been issued; and issuing a scorenotification for the different alert in response to the determinedstatistical likelihood exceeding a score threshold, wherein the scorenotification includes the determined statistical likelihood and anidentity of the particular network entity.

A third example embodiment may involve, a non-transitorycomputer-readable medium having instructions stored thereon that, whenexecuted by one or more processors of a computing system disposed withina remote network management platform and configured to support a managednetwork, cause the computing system to carry out operations including:monitoring respective performance of each network entity of a pluralityof network entities of the managed network, each network entity being atleast one of a service of the managed network or a computing device ofthe managed network, wherein each service of the managed networkexecutes on at least one computing device of the managed network; foreach of the plurality of network entities, issuing an alert in responseto determining that the monitored respective performance is below arespective threshold performance level; based on analysis of a firstgroup of one or more issued alerts, determining a statistical likelihoodthat a different alert will be issued for the monitored respectiveperformance of a particular network entity of the plurality for which norespective alert has yet been issued; and issuing a score notificationfor the different alert in response to the determined statisticallikelihood exceeding a score threshold, wherein the score notificationincludes the determined statistical likelihood and an identity of theparticular network entity.

In a fourth example embodiment, a system may include various means forcarrying out each of the operations of the first example embodiment.

These as well as other embodiments, aspects, advantages, andalternatives will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart of an example method, in accordance with exampleembodiments.

FIG. 6 depicts another communication environment involving a remotenetwork management architecture, showing additional detail, inaccordance with example embodiments.

FIG. 7 depicts a remote management server, in accordance with exampleembodiments.

FIG. 8A depicts further aspects of a remote network managementarchitecture, in accordance with example embodiments.

FIG. 8B illustrates an example display presentation of a remotemanagement server, as well as a conceptual representation of an aspectof performance monitoring, in accordance with example embodiments.

FIG. 9 is a flow chart of an example method of performance monitoring,in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. INTRODUCTION

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow its business,innovate, and meet regulatory requirements. The enterprise may find itdifficult to integrate, streamline and enhance its operations due tolack of a single system that unifies its subsystems and data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data and services within the enterprise byway of secure connections. Such an aPaaS system may have a number ofadvantageous capabilities and characteristics. These advantages andcharacteristics may be able to improve the enterprise's operations andworkflow for IT, HR, CRM, customer service, application development, andsecurity.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, delete(CRUD) capabilities. This allows new applications to be built on acommon application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data isstored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

The following embodiments describe architectural and functional aspectsof example aPaaS systems, as well as the features and advantagesthereof.

II. EXAMPLE COMPUTING DEVICES AND CLOUD-BASED COMPUTING ENVIRONMENTS

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations.

In this example, computing device 100 includes processor(s) 102(referred to as “processor 102” for sake of simplicity), memory 104,network interface(s) 106, and an input/output unit 108, all of which maybe coupled by a system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be any type of computer processing unit, such as acentral processing unit (CPU), a co-processor (e.g., a mathematics,graphics, or encryption co-processor), a digital signal processor (DSP),a network processor, and/or a form of integrated circuit or controllerthat performs processor operations. In some cases, processor 102 may bea single-core processor, and in other cases, processor 102 may be amulti-core processor with multiple independent processing units.Processor 102 may also include register memory for temporarily storinginstructions being executed and related data, as well as cache memoryfor temporarily storing recently-used instructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to register memory and cache memory (which may be incorporatedinto processor 102), as well as random access memory (RAM), read-onlymemory (ROM), and non-volatile memory (e.g., flash memory, hard diskdrives, solid state drives, compact discs (CDs), digital video discs(DVDs), and/or tape storage). Other types of memory may includebiological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1, memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and busses), of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs.

Network interface(s) 106 may take the form of a wireline interface, suchas Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Networkinterface(s) 106 may also support communication over non-Ethernet media,such as coaxial cables or power lines, or over wide-area media, such asSynchronous Optical Networking (SONET) or digital subscriber line (DSL)technologies. Network interface(s) 106 may also take the form of awireless interface, such as IEEE 802.11 (Wifi), BLUETOOTH®, globalpositioning system (GPS), or a wide-area wireless interface. However,other forms of physical layer interfaces and other types of standard orproprietary communication protocols may be used over networkinterface(s) 106. Furthermore, network interface(s) 106 may comprisemultiple physical interfaces. For instance, some embodiments ofcomputing device 100 may include Ethernet, BLUETOOTH®, and Wifiinterfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with example computing device 100. Input/output unit 108 mayinclude one or more types of input devices, such as a keyboard, a mouse,a touch screen, and so on. Similarly, input/output unit 108 may includeone or more types of output devices, such as a screen, monitor, printer,and/or one or more light emitting diodes (LEDs). Additionally oralternatively, computing device 100 may communicate with other devicesusing a universal serial bus (USB) or high-definition multimediainterface (HDMI) port interface, for example.

In some embodiments, one or more instances of computing device 100 maybe deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2, operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purpose of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units ofcluster data storage 204. Other types of memory aside from drives may beused.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via cluster network 208, and/or (ii) network communicationsbetween the server cluster 200 and other devices via communication link210 to network 212.

Additionally, the configuration of cluster routers 206 can be based atleast in part on the data communication requirements of server devices202 and data storage 204, the latency and throughput of the localcluster network 208, the latency, throughput, and cost of communicationlink 210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of thesystem architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from cluster data storage 204. This transmission and retrieval maytake the form of SQL queries or other types of database queries, and theoutput of such queries, respectively. Additional text, images, video,and/or audio may be included as well. Furthermore, server devices 202may organize the received data into web page representations. Such arepresentation may take the form of a markup language, such as thehypertext markup language (HTML), the extensible markup language (XML),or some other standardized or proprietary format. Moreover, serverdevices 202 may have the capability of executing various types ofcomputerized scripting languages, such as but not limited to Perl,Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP),JavaScript, and so on. Computer program code written in these languagesmay facilitate the providing of web pages to client devices, as well asclient device interaction with the web pages.

III. EXAMPLE REMOTE NETWORK MANAGEMENT ARCHITECTURE

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents, managed network 300, remote network management platform 320,and third-party networks 340, all connected by way of Internet 350.

Managed network 300 may be, for example, an enterprise network used by abusiness for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include various client devices 302,server devices 304, routers 306, virtual machines 308, firewall 310,and/or proxy servers 312. Client devices 302 may be embodied bycomputing device 100, server devices 304 may be embodied by computingdevice 100 or server cluster 200, and routers 306 may be any type ofrouter, switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices and services therein, while allowing authorizedcommunication that is initiated from managed network 300. Firewall 310may also provide intrusion detection, web filtering, virus scanning,application-layer gateways, and other services. In some embodiments notshown in FIG. 3, managed network 300 may include one or more virtualprivate network (VPN) gateways with which it communicates with remotenetwork management platform 320 (see below).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server device that facilitatescommunication and movement of data between managed network 300, remotenetwork management platform 320, and third-party networks 340. Inparticular, proxy servers 312 may be able to establish and maintainsecure communication sessions with one or more customer instances ofremote network management platform 320. By way of such a session, remotenetwork management platform 320 may be able to discover and manageaspects of the architecture and configuration of managed network 300 andits components. Possibly with the assistance of proxy servers 312,remote network management platform 320 may also be able to discover andmanage aspects of third-party networks 340 that are used by managednetwork 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operators ofmanaged network 300. These services may take the form of web-basedportals, for instance. Thus, a user can securely access remote networkmanagement platform 320 from, for instance, client devices 302, orpotentially from a client device outside of managed network 300. By wayof the web-based portals, users may design, test, and deployapplications, generate reports, view analytics, and perform other tasks.

As shown in FIG. 3, remote network management platform 320 includes fourcustomer instances 322, 324, 326, and 328. Each of these instances mayrepresent a set of web portals, services, and applications (e.g., awholly-functioning aPaaS system) available to a particular customer. Insome cases, a single customer may use multiple customer instances. Forexample, managed network 300 may be an enterprise customer of remotenetwork management platform 320, and may use customer instances 322,324, and 326. The reason for providing multiple instances to onecustomer is that the customer may wish to independently develop, test,and deploy its applications and services. Thus, customer instance 322may be dedicated to application development related to managed network300, customer instance 324 may be dedicated to testing theseapplications, and customer instance 326 may be dedicated to the liveoperation of tested applications and services.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures have several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may impact all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that impact one customer will likely impact all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other customerinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In order to support multiple customer instances in an efficient fashion,remote network management platform 320 may implement a plurality ofthese instances on a single hardware platform. For example, when theaPaaS system is implemented on a server cluster such as server cluster200, it may operate a virtual machine that dedicates varying amounts ofcomputational, storage, and communication resources to instances. Butfull virtualization of server cluster 200 might not be necessary, andother mechanisms may be used to separate instances. In some examples,each instance may have a dedicated account and one or more dedicateddatabases on server cluster 200. Alternatively, customer instance 322may span multiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

Third-party networks 340 may be remote server devices (e.g., a pluralityof server clusters such as server cluster 200) that can be used foroutsourced computational, data storage, communication, and servicehosting operations. These servers may be virtualized (i.e., the serversmay be virtual machines). Examples of third-party networks 340 mayinclude AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote networkmanagement platform 320, multiple server clusters supporting third-partynetworks 340 may be deployed at geographically diverse locations forpurposes of load balancing, redundancy, and/or high availability.

Managed network 300 may use one or more of third-party networks 340 todeploy services to its clients and customers. For instance, if managednetwork 300 provides online music streaming services, third-partynetworks 340 may store the music files and provide web interface andstreaming capabilities. In this way, the enterprise of managed network300 does not have to build and maintain its own servers for theseoperations.

Remote network management platform 320 may include modules thatintegrate with third-party networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources and provide flexible reporting forthird-party networks 340. In order to establish this functionality, auser from managed network 300 might first establish an account withthird-party networks 340, and request a set of associated resources.Then, the user may enter the account information into the appropriatemodules of remote network management platform 320. These modules maythen automatically discover the manageable resources in the account, andalso provide reports related to usage, performance, and billing.

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and customer instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4, customer instance 322is replicated across data centers 400A and 400B. These data centers maybe geographically distant from one another, perhaps in different citiesor different countries. Each data center includes support equipment thatfacilitates communication with managed network 300, as well as remoteusers.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC). Firewall404A may be configured to allow access from authorized users, such asuser 414 and remote user 416, and to deny access to unauthorized users.By way of firewall 404A, these users may access customer instance 322,and possibly other customer instances. Load balancer 406A may be used todistribute traffic amongst one or more physical or virtual serverdevices that host customer instance 322. Load balancer 406A may simplifyuser access by hiding the internal configuration of data center 400A,(e.g., customer instance 322) from client devices. For instance, ifcustomer instance 322 includes multiple physical or virtual computingdevices that share access to multiple databases, load balancer 406A maydistribute network traffic and processing tasks across these computingdevices and databases so that no one computing device or database issignificantly busier than the others. In some embodiments, customerinstance 322 may include VPN gateway 402A, firewall 404A, and loadbalancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, customer instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4, data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof customer instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of customer instance 322 with one or more Internet Protocol(IP) addresses of data center 400A may re-associate the domain name withone or more IP addresses of data center 400B. After this re-associationcompletes (which may take less than one second or several seconds),users may access customer instance 322 by way of data center 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access customerinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4, configuration items 410 may refer toany or all of client devices 302, server devices 304, routers 306, andvirtual machines 308, any applications, programs, or services executingthereon, as well as relationships between devices and services. Thus,the term “configuration items” may be shorthand for any physical orvirtual device or service remotely discoverable or managed by customerinstance 322, or relationships between discovered devices and services.Configuration items may be represented in a configuration managementdatabase (CMDB) of customer instance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and customer instance 322,or security policies otherwise suggest or require use of a VPN betweenthese sites. In some embodiments, any device in managed network 300and/or customer instance 322 that directly communicates via the VPN isassigned a public IP address. Other devices in managed network 300and/or customer instance 322 may be assigned private IP addresses (e.g.,IP addresses selected from the 10.0.0.0-10.255.255.255 or192.168.0.0-192.168.255.255 ranges, represented in shorthand as subnets10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. EXAMPLE DEVICE AND SERVICE DISCOVERY

In order for remote network management platform 320 to administer thedevices and services of managed network 300, remote network managementplatform 320 may first determine what devices are present in managednetwork 300, the configurations and operational scorees of thesedevices, and the services provided by the devices, and well as therelationships between discovered devices and services. As noted above,each device, service, and relationship may be referred to as aconfiguration item. The process of defining configuration items withinmanaged network 300 is referred to as discovery, and may be facilitatedat least in part by proxy servers 312.

For purpose of the embodiments herein, a “service” may refer to aprocess, thread, application, program, server, or any other softwarethat executes on a device. A “service” may also refer to a high-levelcapability provided by multiple processes, threads, applications,programs, and/or servers on one or more devices working in conjunctionwith one another. For example, a high-level web service may involvemultiple web application server threads executing on one device andaccessing information from a database service that executes on anotherdevice. The distinction between different types or levels of servicesmay depend upon the context in which they are presented.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, third-party networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within customerinstance 322. Customer instance 322 may transmit discovery commands toproxy servers 312. In response, proxy servers 312 may transmit probes tovarious devices and services in managed network 300. These devices andservices may transmit responses to proxy servers 312, and proxy servers312 may then provide information regarding discovered configurationitems to CMDB 500 for storage therein. Configuration items stored inCMDB 500 represent the environment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of customer instance 322. As discovery takes place,task list 502 is populated. Proxy servers 312 repeatedly query task list502, obtain the next task therein, and perform this task until task list502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,customer instance 322 may store this information in CMDB 500 and placetasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices and services in managed network 300 asconfiguration items 504, 506, 508, 510, and 512. As noted above, theseconfiguration items represent a set of physical and/or virtual devices(e.g., client devices, server devices, routers, or virtual machines),services executing thereon (e.g., web servers, email servers, databases,or storage arrays), relationships therebetween, as well as higher-levelservices that involve multiple individual configuration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,as a set of LINUX®-specific probes may be used. Likewise if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (services), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice and service is available in CMDB 500. For example, afterdiscovery, operating system version, hardware configuration and networkconfiguration details for client devices, server devices, and routers inmanaged network 300, as well as services executing thereon, may bestored. This collected information may be presented to a user in variousways to allow the user to view the hardware composition and operationalscore of devices, as well as the characteristics of services.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For instance, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsbe displayed on a web-based interface and represented in a hierarchicalfashion. Thus, adding, changing, or removing such dependencies andrelationships may be accomplished by way of this interface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the customer instance is populated, for instance,with a range of IP addresses. At block 522, the scanning phase takesplace. Thus, the proxy servers probe the IP addresses for devices usingthese IP addresses, and attempt to determine the operating systems thatare executing on these devices. At block 524, the classification phasetakes place. The proxy servers attempt to determine the operating systemversion of the discovered devices. At block 526, the identificationphase takes place. The proxy servers attempt to determine the hardwareand/or software configuration of the discovered devices. At block 528,the exploration phase takes place. The proxy servers attempt todetermine the operational state and services executing on the discovereddevices. At block 530, further editing of the configuration itemsrepresenting the discovered devices and services may take place. Thisediting may be automated and/or manual in nature.

The blocks represented in FIG. 5B are for purpose of example. Discoverymay be a highly configurable procedure that can have more or fewerphases, and the operations of each phase may vary. In some cases, one ormore phases may be customized, or may otherwise deviate from theexemplary descriptions above.

V. EXAMPLE OPERATIONAL PERFORMANCE ANALYTICS

One of the operational missions of network management is monitoring andmanaging performance of the managed network, including its manageabledevices and the applications and services that run on them, togetherreferred to herein as manageable network entities. Functions and tasksthat carry out this mission can be implemented and/or coordinated withina customer instance for a given managed network. In an exampleembodiment, performance monitoring and management may be configured aspart of a remote network management platform 320.

There may also be components and/or elements of performance monitoringand management that deployed within the managed network. For example,routers, proxy servers, or other devices could run programs or servicesthat communicate with manageable network entities in the managed networkin order to collect and report performance metrics. The programs orservices could also carry out some evaluation and/or analysis ofcollected performance metrics. For example, performance metrics could becompared with defined thresholds or target performance levels, andalerts or other event notifications could be generated in response todetecting observed performance levels below one or another thresholdand/or beyond or outside of one or another defined operating range. Inan example embodiment, SNMP could be used to collect performance metricsfrom manageable network entities. Other management protocols could beused instead or as well.

In accordance with example embodiments, performance monitoring andmanagement of a managed network may advantageously integrate performanceforecasting and prediction with real-time, near-real-time, andhistorical performance monitoring and analysis. This integration ofpast, current, and predicted performance of a managed network and itsconstituent manageable entities is referred to herein as “operationalperformance analysis,” and may be implemented on one or more computingsystems and/or platforms within a remote management platform. Byproviding performance forecasting and prediction, operationalperformance analytics can anticipate potential problems with performanceand/or operation of manageable network entities before they occur,providing an opportunity to take preemptive actions.

In accordance with example embodiments, operational performance analysismay be implemented in computing system that is part of a remote networkmanagement platform 320. The computing system may include one or moreprocessors, memory, and program instructions that, when executed by theone or more processors, cause the computing system to carry out variousoperations and functions of operational performance analysis describedherein.

In an example embodiment, the computing system that implementsoperational performance analysis may be part of a remote managementserver. This is illustrated in FIG. 6, in which a remote managementserver takes the form of a customer instance management server 600within, or as part of, the customer instance 322. As in FIG. 5A, thecustomer instance 322 also includes the CMDB 500 and task list 502. Thecustomer instance management server 600 can access and/or consult theCMDB 500, enabling it to gain information about the configuration items504-512 of the managed network 300 that is managed by the customerinstance 322. The customer instance management server 600 can use thisinformation to query or command one or more of the configuration itemsfor status and performance information by way of the proxy servers 312,as indicated. Status and performance data may be returned in reply, asalso indicated. As noted above, protocols such as SNMP could be used tomake such queries and to collect performance statistics about theconfiguration items 504-512. Based on monitored performance data, thecustomer instance management server 600 could carry out variousoperations and function of operational performance analysis.

In example operation, the customer instance management server 600 couldcommunicate with a proxy server 312, a router 306, or other intermediarydevice in the managed network 300 to invoke actions for requestingand/or demanding performance data from one or more manageable devices,such as operational metrics indicative of one or more levels ofperformance of the manageable devices. For example, manageable devicescould report processor (e.g. CPU) load, memory usage, program capacityloading and/or utilization, faults, events, and retransmissionstatistics, among other non-limiting examples of performance statistics.Performance data could be reported directly back to the customerinstance management server 600 in “raw” (e.g., unprocessed orunanalyzed) form. Raw data may then be analyzed, for example todetermine what thresholds may have been exceeded or what targetperformance levels may have failed to have been achieved. Additionallyor alternatively performance data could be reported directly back to thecustomer instance management server 600 with some degree of analysis orprocessing, such as indications about thresholds and (under)achievementof target performance levels.

FIG. 7 illustrates an example customer instance management server 600,in accordance with example embodiments. The arrangement shown could alsoapply more generally to other implementations of a remote managementserver. As shown, the customer instance management server 600 includes acomputing system 700 that, in turn, includes a computing device 100, auser I/O interface 702, and a management display 704. The user I/Ointerface 702 may include a keyboard and mouse, as well as other devicesenabling a user interact with the computing system 100. The managementdisplay 704 includes a display device for displaying performance andmonitoring data, such as metrics, and other performance indicators,described below in more detail. While the management display 704 may beconsidered an output device, it is shown separately from the user I/O702 to facilitated discussion of specific features and functions ofoperational performance analysis, also discussed below.

In accordance with example embodiments, processing and/or analysis ofperformance metrics, and comparisons with various thresholds, targetlevels, and ranges, could be used to generate alerts. More particularly,alerts can be used to signal that operational performance of one or moremanageable entities has fallen below some threshold level, failed toachieve at least some target level, or failed to maintain someoperational range. Other types of performance criteria could be appliedto indicate conditions signaled by alerts. In addition, alerts for agiven manageable network entity could also indicate a severity level,depending on a degree or amount by which observed (monitored)performance deviates, diverges, or departs from a threshold, targetlevel, or operating range, for example.

Alerts can be used in operational performance analysis to uncover andmonitor performance dependencies between two or more manageable networkentities, providing a basis for predicting or forecasting future alertsfor performance of a particular network entity based on observed alertsfor performance of other network entities. In accordance with exampleembodiments, alerts for performance of two or more network entities maybe grouped together based on either known or empirically-derived causalassociations between them. Known causal associations may be establishedbased, for example, on known functional dependencies between variousnetwork entities whose respective performances are tracked by a set ofalerts. The alerts of the set may thus be grouped based on the knowndependencies. The knowledge of the functional dependencies could bebased known network configurations of the network entities and/or knownoperational flow between network entities, for example. In an exampleembodiment, this information could be recorded in the CMDB 500.Empirically-derived causal associations may be established based onobservations of alerts over time. For example, it may be observed byoperations management personnel that certain alerts tend to occur in afixed or nearly-fixed sequence. This observation may then suggestgrouping these certain alerts together. As described below, grouping ofalerts can be an automated or semi-automated process, an interactiveprocess involving user selections, or some combination thereof.

FIG. 8A depicts further aspects of a remote network managementarchitecture of a managed network 300 that illustrate some examples ofhow and where interdependencies can manifest. In this example, themanaged network 300 is shown to include two sub-networks 800-A and800-B. For the sake of brevity in the figure, only one customer instance322 is depicted and third-party networks 340 is omitted. Thesub-networks 800-A and 800-B each include illustrative manageablenetwork entities having illustrative interconnections indicated bybi-directional arrows. Thus, for example, sub-network 800-A includes asales transaction server 802 connected with a load balancer 806, whichin turn is connected with a sales database 804 and a router 808. Also byway of example, the sub-network 800-B includes an inventory server 812connected with a router 818. The routers then provide connectivity tothe customer instance 322 by way of the internet 350. It will beappreciated that the manageable network entities and interconnectionsshown in FIG. 7 are representative, and that there could be others aswell.

In an example scenario, operations involving the sales transactionserver 802 and the sales database 804 may have performanceinterdependencies on each other, and both have dependencies on the loadbalancer 806. All three may have dependencies on the router 808.Similarly, the inventory server 812 may have dependencies on the router818. There could be interdependencies across sub-networks as well. Forexample, there could be operations or functions of the sales transactionserver 802 on the inventor server 812. These are just some illustrativeexamples.

In accordance with example embodiments, the various dependencies andinterdependencies may be captured or discoverable from information inthe CMBD 500, or possibly through the process described above for howthe CMDB 500 is created. As such, the CMBD 500 (or other database ofmanageable network entities and of discovered network architecture) canprovide information for recognizing and/or learning which manageablenetwork entities may have alerts that are, or are likely to be,associated. Associated alerts may thus be grouped, and their occurrencestatistics analyzed to derive performance prediction and forecastingprobabilities.

More particularly, alerts in a group can be treated as statisticalperformance data, which may then be analyzed to quantitatively determineconditional probabilities connecting their respective occurrences. Theconditional probabilities then form the basis for predicting futureoccurrences of one more alerts of the group based on observedoccurrences of other alerts of the group. As described above, each alertin a group may represent an occurrence of a particular manageablenetwork entity having performance below a threshold or target level.Thus, given an occurrence of an alert for one manageable network entity,the likelihood that an alert will occur for a different manageablenetwork entity in the group can be determined according to a conditionalprobability connecting the alerts.

In accordance with example embodiments, conditional probabilitiesconnecting pairs of alerts of a group, where each alert of the pair isassociated with a some type of sub-target performance for a differentmanageable network entity, can be determined by computing a jointprobability distribution for the alerts in the group. Various analyticaltechniques for estimating a joint probability may be cast in terms ofthe conditional probabilities. Thus, the conditional probabilities maybe derived from the estimated (computed) joint probability distribution.

In an example embodiment, the joint probability of the alerts in a groupmay be estimated as a Chow-Liu tree and using known analytical andalgorithmic techniques. In general, this approach may be applied to adata set of N variables {x₁, x₂, . . . , x_(N)} represented as anN-dimensional vector x=(x₁, x₂, . . . , x_(N)) and having jointprobability distribution P(x). In a tree model, each variable defines anode or vertex in a tree, and pairs of nodes are connected by edges orbranches that correspond to a statistical weight of the connection. Eachnode may have one parent but more than one child. The joint probabilitymay be approximated as P_(t)(x)=Π_(i=1) ^(N)P(x_(i)|x_(π(i))), wherex_(π(i)) corresponds the parent of x_(i) (which is the empty set for theroot node of the tree). The approximated joint distribution is thenestimated by minimizing the Kullback-Leibler divergence (or KLdivergence) between the estimated distribution P_(t)(x) and assumedactual joint distribution P(x), where the KL divergence is given by

${{KL}\left( {P,P_{t}} \right)} = {\sum\limits_{i = 1}^{N}{{P(x)}\log {\frac{P(x)}{P_{t}(x)}.}}}$

In a Chow-Liu tree, the edges or branches connecting the nodescorrespond to the mutual information between the variables of theconnected nodes, and is given by

${I\left( {x_{i},x_{j}} \right)} = {\sum\limits_{x_{i},x_{j}}^{\;}{{P\left( {x_{i},x_{j}} \right)}{{\log\left( \frac{P\left( {x_{i},x_{j}} \right)}{{P\left( x_{i} \right)}{P\left( x_{j} \right)}} \right)}.}}}$

The joint distribution may then be determined using a well-knownefficient computational technique referred to as the Chow-Liu algorithm.

In applying the Chow-Liu algorithm to operational performance analysis,the alerts within a group are taken to be the data set {x₁, x₂, . . . ,x_(N)}, so that each node in the tree represents an occurrence of analert for some type of sub-target performance for one of the manageablenetwork entities associated with the alerts in the group. As such, thestatistical weights represented by the branches correspond to the mutualinformation between pairs of alerts associated with different manageablenetwork entities of the group. The derived conditional probabilities aretherefore indicative of the likelihood that an occurrence of one alertassociated with one network entity will be followed by an occurrence ofan alert associated with different network entity. Advantageously, theconditional probabilities therefore provide for prediction and/orforecasting of performance alerts based on observed alerts.

In accordance with example embodiments, a group of alerts may be createdor generated by selecting alerts dynamically in real-time, innear-real-time, from historical data, or some combination of theseselection procedures. Analysis of a joint probability of alerts in agroup can similarly be carried out on real-time data, near-real-timedata, historical data, or some combination of temporal time scales ofthe alert data of a group. Alerts may be selected for inclusion in agroup based at least in part on known or suspected causal associations.In real-time or near-real time selection, alerts may be observed by auser during the course of active performance monitoring, and thenselected in an interactive procedure by a user. For example, it may beobserved over some period of time that a particular alert is frequentlyissued after one or more other particular alerts are issued. Such anobservation may then provide a basis for grouping the alerts that haveappeared together in previous instances. Once grouped, an analysis asdescribed above may be carried out to quantify the probabilisticrelationship among the alerts in the group.

Analysis of historical data can also uncover causal associations andprovide criteria for inclusion in a group. Again, analysis can thenquantify the probabilistic relationship among the alerts in such ahistory-based group. Again, a user may create a group through aninteractive process applied to the historical data. Additionally oralternative, an automated or semi-automated process could be applied tothe historical data that uncovers likely causal associations and usesthe results to generate a group. Other automated, or semi-automated, orinteractive processes could also take account of known networkarchitecture, operational interdependencies, and/or data/informationflows to identify likely causal associations and uses the results togenerate groups.

A representation of active alert monitoring in accordance with exampleembodiments is shown in FIG. 8B, which illustrates an example displaypresentation of a remote management server, as well as a conceptualrepresentation analysis of alerts. The figure shows a customer instancemanagement server 600 and an example dashboard display 830 showing asnapshot of active alert monitoring. The left side of the displaydepicts a status monitor view of alerts for the network entities shownin FIG. 8A, namely, a sales transaction server 802, sales database 804,load balancer 806, router 808, inventory server 812, and router 818.These are listed by host device item number (808, 804, . . . , 818) in afirst column labeled “Device,” and by service in a second column labeled“Service.” A third column displays an alert status (corresponding to thex_(i) data, as indicated) for each device/service as a shaded circle,where the shading represents a severity level of an issued alert. By wayof example, white represents the lowest severity (or possiblycorresponding to “no alert”), and black represents the highest severitylevel; shades of gray in between correspond to a severity levelgradation. To the right of each issued alert, a hatched circlerepresents a predicted alert. It will be appreciated that the dashboarddisplay 830 in FIG. 8B is just an example illustration of what such adisplay could look like and what information is presented. Other formatsand presentations are possible as well.

In the example snapshot of FIG. 8B, alerts for the sales transactionserver 802, the load balancer 806, and the router 818 are shown to bepart of a dynamic group. Evidently and by way of example, at the time ofthe snapshot, alerts of differing severity levels have been issued forthe load balancer 806 and the router 818. No alert has been issued forthe sales transaction server 802, but evidently one has been predictedbased on the analysis of all three alerts. The predicted alert is alsolisted with an example “score” of 77%, which corresponds to a confidencelevel of the prediction. Note that an alert for the sales transactionserver 802 can be included in the group even though no actual alert hasyet been issued. As described above, this selection may be based onpreviously observed behavior, known interdependencies of the alerts, orsome combination thereof.

The right side of the dashboard display 830 shows an example of atemporal “drill-down” view, in which time series data of the actualperformance underlying the alert status is shown. Evidently and by wayof example, the router 818 and load balancer 806 both show upward jumpsin the monitored performance. In the context of the currentlyillustrative example, these jumps could represent a jump in capacityutilization or like that results in performance degradation and theissuing of the alerts that are shown for each device/service. A similarjump and corresponding performance degradation is then predicted for thesales transaction server 802, as also shown in the drill-down view.Using this information, a user such as an operations engineer, couldreceive advanced notice or warning of a potential problem, and takepossible action avoid or alleviate the problem before it occurs. Thissnapshot view illustrates just one aspect of how operational performanceanalytics can be applied to alerts to predict and/or forecast problemsbefore they occur.

The lower left side of FIG. 8B shows a conceptual depiction of theanalysis of alerts carried out by the customer instance managementserver 600. Following from the discussion above of analysis using aChow-Liu tree, the conceptual illustration shows an representative treein which the nodes are the alert (e.g., {x₁, x₂, . . . , x₆} and thejoint probability distribution is computed according the conditionalprobabilities. In accordance with example embodiments, each predictionof a future alert derived from the conditional probabilities can bereported as a score that gives a statistical confidence level of theprediction of a future alert. This is illustrated by way of example inthe figure, as indicated by the “77%” next to the predicted alert.

In accordance with example embodiments, selection of alerts forinclusion in a group can be an interactive process or an automated orsemi-automated process. Either approach could evaluate active alertsand/or historical alerts to uncover empirical evidence of causalrelations between alerts. In an example embodiment, there may be morethan one interactive monitoring server or station, each possibly with adifferent view of alerts. This embodiment may further support sharinggroup selections among different users at the different monitoringstations. For example, a user at one station may share a group selectionwith a user at a different station. In this way, different users canmake each other aware of alerts that they are seeing, and therebyquickly and efficiently propagate forecasts or predictions of impendingissues or problems.

Also in accordance with example embodiments, various performancethresholds, target performance levels, and target performance ranges maybe set and adjusted interactively by one or more users. For example, theseverity levels illustrated in FIG. 8B could correspond to gradations ofthresholds for issuing alerts. By way of example, the white circle couldcorrespond to a capacity utilization of less than 10%, while the blackcircle could correspond to a capacity utilization of 90%, and thegradations in between could correspond to capacity utilizations of 30%,50%, and 70%. An alert of a given gradation could then be issued forobserved/monitored capacity utilization greater than the correspondingthreshold for the given gradation. Other values could be used, and theycould be adjusted dynamically by a user. There could be other thresholdsas well. For example, there could be a temporal threshold to define howlong a given condition should or must persist before an appropriatealert is issued.

In further accordance with example embodiments, a threshold score couldbe used to determine if and when an alert prediction should be issued.For example, a score threshold of 40% for a particular network entitycould correspond to a requirement that only alert predictions withscores of at least 40% should be issued for performance of theparticular network entity. Again, score thresholds could be setinteractively by users, and values other than 40% could be used.

In a similar manner, target performance levels and ranges could also beset interactively. Further, performance thresholds and targetperformance levels could be used as lower bounds, such that a observedor monitored performance would need to fall below a threshold or targetlevel in order to trigger an alert. Target ranges could be similarlyused to define acceptable operation either within a range or insteadoutside of a range.

In accordance with example embodiments, alerts may be generated byvarious network devices and/or services, as well as at different timesrelative to the performance that is being monitored. For example, amanageable network entity could incorporate a performance monitoringfunction or application capable of self-monitoring one or moreperformance metrics, and generating one or more alerts according to oneor more thresholds, targets, or ranges. A remote management server, suchas the customer instance management server 600, could send commands tothe network entity to set up the self-monitoring, and later receivemonitoring data, including alerts, from the network entity. As anotherexample, network entities of a managed network could report rawperformance metrics to an intermediary device, such as the proxy server312, which could process the raw data according to one or morethresholds. The intermediary device could then report alerts back to aremote management server. In this example, the remote management servercould communicate with the intermediary device to set up thresholds,targets, and ranges, and to receive alerts and other performancemonitoring data. As still another example, raw performance metrics andmonitoring data could be reported back to a remote management server,which could then apply the processing to detect conditions for issuingalerts. In any or all of these examples, analysis of performance datafor determination of alerts could be carried out in real-time ornear-real time as performance is actively monitored and metrics arecollected. Additionally or alternatively, analysis could be carried outafter the fact on historical performance data. Combinations of theseexamples could be implemented if a remote network management system, aswell as other techniques.

VI. EXAMPLE OPERATIONS

FIG. 9 is a flow chart of an example method of performance monitoring,in accordance with example embodiments. The method illustrated by FIG. 9may be carried out by a computing system disposed within a remotenetwork management platform and configured to support a managed networkthat includes manageable network entities. Non-limiting examples of acomputing system include the computing system 700 in the customerinstance management server 600 illustrated in FIG. 7, and more generallyin computing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the method can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The example method may be implemented as machine language or other formsof computer-readable programmatic instructions stored in memory andaccessible to one or more processors of the computing device orcomputing system that, when executed by one or more processors of thecomputing device or computing system, cause the computing device orcomputing system to carry out the various steps, functions, and/oroperations described herein. The machine language or other forms ofprogrammatic instructions may further be stored on tangible,non-transitory computer-readable medium for delivery to and loading inone or more computing systems for subsequent execution.

The embodiments of FIG. 9 may be simplified by the removal of any one ormore of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

At block 920, the computing system monitors respective performance ofeach network entity of a plurality of network entities of the managednetwork. In accordance with example embodiments, each network entity maybe a service of the managed network and/or a computing device of themanaged network. Each service of the managed network may execute or beconfigured to execute on at least one computing device of the managednetwork.

At block 922, for each of the plurality of network entities, thecomputing system issues an alert in response to determining that themonitored respective performance is below a respective thresholdperformance level.

At block 924, the computing system, based on analysis of a first groupof one or more issued alerts, determines a statistical likelihood that adifferent alert will be issued for the monitored respective performanceof a particular network entity of the plurality for which no respectivealert has yet been issued.

Finally, at block 926, the computing system issues a score notificationfor the different alert in response to the determined statisticallikelihood exceeding a score threshold. In accordance with exampleembodiments, the score notification may include the determinedstatistical likelihood and an identity of the particular network entity.

In accordance with example embodiments, the example method could also beapplied to one or more additional groups of issued alerts. For example,based on analysis of a second group of one or more issued alerts, thecomputing system could determine another statistical likelihood thatanother different alert will be issued for the monitored respectiveperformance of another particular network entity of the plurality forwhich no respective alert has yet been issued. The computing systemcould then issue another score notification for the other differentalert in response to the determined other statistical likelihoodexceeding another score threshold. As with the score notification ofblock 926, this other score notification could include the determinedother statistical likelihood and an identity of the other particularnetwork entity.

In accordance with example embodiments, the remote management platformcould be communicatively connected to the plurality of network entitiesvia an intermediary network device in the managed network, such as theproxy servers 312. Thus, monitoring the respective performance of eachnetwork entity of the plurality of network entities could entailtransmitting one or more messages from the computing system to theintermediary network device requesting performance metrics of one ormore network entities of the plurality, and thereafter receiving datatransmitted from the intermediary network device indicative of therequested performance metrics. In this way, the remote managementplatform or its computing system could effectively control or influencethe intermediary device, causing it to acquire or obtain monitoring datafrom the network entities.

For example, referring to FIGS. 3 and 5A, the management platform send amessage to router 306 by way of a proxy server 312, causing the router306 to carry out or more SNMP management functions in order to collectperformance metrics from one or more configuration items (e.g.,manageable network entities). The metrics, or possibly some derivequantity or indicator thereof, could be returned to management platform.Other management functions/operations and/or devices could be usedinstead or as well.

Also in accordance with example embodiments, determining that themonitored respective performance is below the respective thresholdperformance level could entail determining that a metric indicative ofperformance either below a target threshold, or outside of a targetoperating range. That is, the meaning of “below the respectiveperformance level” can be viewed as encompassing comparison to a singlethreshold level or to a defined range.

In accordance with example embodiments, determining the statisticallikelihood based on analysis of the first group of one or more issuedalerts could entail determining a respective conditional probability foreach of the respective issued alerts. Specifically, each conditionalprobability could be a probability that the monitored respectiveperformance of the particular network entity will be below a particularthreshold performance level given an occurrence of issuance of therespective issued alert.

In further accordance with example embodiments, determining therespective conditional probability could entail compiling a historicalrecord of alerts issued for both (i) the monitored respectiveperformance of the network entities of the plurality associated with thefirst group of the one or more issued alerts, and (ii) the monitoredrespective performance of the particular network entity. Then, a jointprobability distribution of the historical record could be computed, andthe respective conditional probability derived from the computed jointprobability distribution. In an example embodiment, computing the jointprobability distribution of the historical record could be accomplishedby computing an analytic approximation of the joint probabilitydistribution as a Chow-Liu tree.

In still further accordance with example embodiments, the example methodcould further include actions or operations taken prior to determiningthe statistical likelihood. Specifically, the first group of the one ormore issued alerts could be identified prior to determining thestatistical likelihood. The different alert could be identified as apotential alert that is associated with the first group, also prior todetermining the statistical likelihood. In an example embodiment,identifying the different alert as a potential alert associated with thefirst group could correspond to determining a causal relation betweenthe first group and the potential alert.

In further accordance with example embodiments, identifying andassociating alerts could be an interactive operation performed, forexample, by a user monitoring the network by way of input/outputfacilities and functions of the computing system. Thus, identifying thefirst group of issued alerts from among a plurality of issued alertscould entail displaying the plurality of issued alerts on theinteractive display device, and then using an interactive cursor of theinteractive display device to select the first group of issued alertsfrom among the displayed plurality of issued alerts.

In accordance with example embodiments, issuing a score notification forthe different alert could entail computing a prediction score as astatistical confidence level that an alert will be issued for themonitored respective performance of the particular network entity. Theprediction score could then be reported, for example in the form of adisplay on a display device.

In further accordance with example embodiments the example method couldalso include setting the respective threshold performance level of eachof the plurality of network entities, as well as setting the scorethreshold. In this way, a user could set target levels of performancemetrics at which active alerts would be issued, and at which predictionsof future alerts would be issued.

A. Other Variations and Embodiments

The example computing devices, platforms, network entities, and the likedescribed above represent individually and/or collectively device meansfor carrying out the various operations, functions, and methodsdescribed herein. Similarly, the example methods described in connectionwith FIGS. 5B and 9 represent operational means, when made operationalon one or more of the device means, for implementing the various exampleembodiments described herein.

VII. CONCLUSION

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, solid statedrives, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A computing system disposed within a remotenetwork management platform and configured to support a managed network,the computing system comprising: one or more processors; memory; andprogram instructions, stored in the memory, that upon execution by theone or more processors cause the computing system to perform operationsincluding: monitoring respective performance of each network entity of aplurality of network entities of the managed network, each networkentity being at least one of a service of the managed network or acomputing device of the managed network, wherein each service of themanaged network executes on at least one computing device of the managednetwork; for each of the plurality of network entities, issuing an alertin response to determining that the monitored respective performance isbelow a respective threshold performance level; based on analysis of afirst group of one or more issued alerts, determining a statisticallikelihood that a different alert will be issued for the monitoredrespective performance of a particular network entity of the pluralityfor which no respective alert has yet been issued; and issuing a scorenotification for the different alert in response to the determinedstatistical likelihood exceeding a score threshold, wherein the scorenotification includes the determined statistical likelihood and anidentity of the particular network entity.
 2. The computing system ofclaim 1, wherein the operations further include: based on analysis of asecond group of one or more issued alerts, determining anotherstatistical likelihood that another different alert will be issued forthe monitored respective performance of another particular networkentity of the plurality for which no respective alert has yet beenissued; and issuing another score notification for the other differentalert in response to the determined other statistical likelihoodexceeding another score threshold, wherein the other score notificationincludes the determined other statistical likelihood and an identity ofthe other particular network entity.
 3. The computing system of claim 1,wherein the remote management platform is communicatively connected tothe plurality of network entities via an intermediary network device inthe managed network, and wherein monitoring the respective performanceof each network entity of the plurality of network entities comprises:transmitting one or more messages from the computing system to theintermediary network device requesting performance metrics of one ormore network entities of the plurality; and receiving data transmittedfrom the intermediary network device indicative of the requestedperformance metrics.
 4. The computing system of claim 1, whereindetermining that the monitored respective performance is below therespective threshold performance level comprises determining that ametric indicative of performance is one of: below a target threshold, oroutside of a target operating range.
 5. The computing system of claim 1,wherein determining the statistical likelihood based on analysis of thefirst group of one or more issued alerts comprises: for each respectiveissued alert of the one or more issued alerts, determining a respectiveconditional probability that the monitored respective performance of theparticular network entity will be below a particular thresholdperformance level given an occurrence of issuance of the respectiveissued alert.
 6. The computing system of claim 5, wherein determiningthe respective conditional probability that the monitored respectiveperformance of the particular network entity will be below theparticular threshold performance level given the occurrence of theissuance of the respective issued alert comprises: compiling ahistorical record of alerts issued for both (i) the monitored respectiveperformance of the network entities of the plurality associated with thefirst group of the one or more issued alerts, and (ii) the monitoredrespective performance of the particular network entity; computing ajoint probability distribution of the historical record; and derivingthe respective conditional probability from the computed jointprobability distribution.
 7. The computing system of claim 5, whereincomputing the joint probability distribution of the historical recordcomprises computing an analytic approximation of the joint probabilitydistribution as a Chow-Liu tree.
 8. The computing system of claim 1,wherein the operations further include: prior to determining thestatistical likelihood, identifying the first group of the one or moreissued alerts from among a plurality of issued alerts; and prior todetermining the statistical likelihood, identifying the different alertas a potential alert that is associated with the first group of the oneor more issued alerts.
 9. The computing system of claim 8, whereinidentifying the different alert as a potential alert that is associatedwith the first group of the one or more issued alerts comprisesdetermining a causal relation between the first group of the one or moreissued alerts and the potential alert.
 10. The computing system of claim8, wherein the computing system further comprises an interactive displaydevice, and wherein identifying the first group of the one or moreissued alerts from among a plurality of issued alerts comprises:displaying the plurality of issued alerts on the interactive displaydevice; and using an interactive cursor of the interactive displaydevice to select the first group of the one or more issued alerts fromamong the displayed plurality of issued alerts.
 11. The computing systemof claim 1, wherein issuing a score notification for the different alertcomprises: computing a prediction score as a statistical confidencelevel that an alert will be issued for the monitored respectiveperformance of the particular network entity; and reporting theprediction score.
 12. The computing system of claim 1, wherein theoperations further include: setting the respective threshold performancelevel of each of the plurality of network entities; and setting thescore threshold.
 13. A computer-implemented method carried out by acomputing system disposed within a remote network management platformand configured to support a managed network, the method comprising:monitoring respective performance of each network entity of a pluralityof network entities of the managed network, each network entity being atleast one of a service of the managed network or a computing device ofthe managed network, wherein each service of the managed networkexecutes on at least one computing device of the managed network; foreach of the plurality of network entities, issuing an alert in responseto determining that the monitored respective performance is below arespective threshold performance level; based on analysis of a firstgroup of one or more issued alerts, determining a statistical likelihoodthat a different alert will be issued for the monitored respectiveperformance of a particular network entity of the plurality for which norespective alert has yet been issued; and issuing a score notificationfor the different alert in response to the determined statisticallikelihood exceeding a score threshold, wherein the score notificationincludes the determined statistical likelihood and an identity of theparticular network entity.
 14. The method of claim 13, wherein theremote management platform is communicatively connected to the pluralityof network entities via an intermediary network device in the managednetwork, and wherein monitoring the respective performance of eachnetwork entity of the plurality of network entities comprises:transmitting one or more messages from the computing system to theintermediary network device requesting performance metrics of one ormore network entities of the plurality; and receiving data transmittedfrom the intermediary network device indicative of the requestedperformance metrics.
 15. The method of claim 13, wherein determining thestatistical likelihood based on analysis of the first group of one ormore issued alerts comprises: compiling a historical record of alertsissued for both (i) the monitored respective performance of the networkentities of the plurality associated with the first group of the one ormore issued alerts, and (ii) the monitored respective performance of theparticular network entity; computing a joint probability distribution ofthe historical record; and for each respective issued alert of the oneor more issued alerts, deriving from the computed joint probabilitydistribution a respective conditional probability that the monitoredrespective performance of the particular network entity will be below aparticular threshold performance level given an occurrence of issuanceof the respective issued alert.
 16. The method of claim 13, wherein thecomputing system further comprises an interactive display device, andwherein the method further comprises: prior to determining thestatistical likelihood, using an interactive cursor of the interactivedisplay device to select the first group of the one or more issuedalerts from among a plurality of issued alerts displayed on theinteractive display device; and prior to determining the statisticallikelihood, identifying the different alert as a potential alert that iscausally related to the first group of the one or more issued alerts.17. A non-transitory computer-readable medium having instructions storedthereon that, when executed by one or more processors of a computingsystem disposed within a remote network management platform andconfigured to support a managed network, cause the computing system tocarry out operations including: monitoring respective performance ofeach network entity of a plurality of network entities of the managednetwork, each network entity being at least one of a service of themanaged network or a computing device of the managed network, whereineach service of the managed network executes on at least one computingdevice of the managed network; for each of the plurality of networkentities, issuing an alert in response to determining that the monitoredrespective performance is below a respective threshold performancelevel; based on analysis of a first group of one or more issued alerts,determining a statistical likelihood that a different alert will beissued for the monitored respective performance of a particular networkentity of the plurality for which no respective alert has yet beenissued; and issuing a score notification for the different alert inresponse to the determined statistical likelihood exceeding a scorethreshold, wherein the score notification includes the determinedstatistical likelihood and an identity of the particular network entity.18. The non-transitory computer-readable medium of claim 17, wherein theremote management platform is communicatively connected to the pluralityof network entities via an intermediary network device in the managednetwork, and wherein monitoring the respective performance of eachnetwork entity of the plurality of network entities comprises:transmitting one or more messages from the computing system to theintermediary network device requesting performance metrics of one ormore network entities of the plurality; and receiving data transmittedfrom the intermediary network device indicative of the requestedperformance metrics
 19. The non-transitory computer-readable medium ofclaim 17, wherein determining the statistical likelihood based onanalysis of the first group of one or more issued alerts comprises:compiling a historical record of alerts issued for both (i) themonitored respective performance of the network entities of theplurality associated with the first group of the one or more issuedalerts, and (ii) the monitored respective performance of the particularnetwork entity; computing a joint probability distribution of thehistorical record; and for each respective issued alert of the one ormore issued alerts, deriving from the computed joint probabilitydistribution a respective conditional probability that the monitoredrespective performance of the particular network entity will be below aparticular threshold performance level given an occurrence of issuanceof the respective issued alert
 20. The non-transitory computer-readablemedium of claim 17, wherein the computing system further comprises aninteractive display device, and wherein the method further comprises:prior to determining the statistical likelihood, using an interactivecursor of the interactive display device to select the first group ofthe one or more issued alerts from among a plurality of issued alertsdisplayed on the interactive display device; and prior to determiningthe statistical likelihood, identifying the different alert as apotential alert that is causally related to the first group of the oneor more issued alerts.